Additional file 1 of Latent classes of early response trajectories to biologics initiation in juvenile idiopathic arthritis: an analysis of four trials
Bibliographic record
Abstract
Additional file 1: Table A1. Summary of Trial Designs for the 4 Included Randomized Controlled Trials (RCT). Figure A1. Early Latent Trajectories of Response in Juvenile Idiopathic Arthritis Patients Following Biologic DMARDs without Membership Predictor. Legend: Response as measured by active joint counts (AJC) that has been transformed = log (AJC + 1.8). Class 1 has high baseline AJC and slow response (19.8%). Class 2 has low baseline AJC and steady response (74.6%). Class 3 has moderate baseline AJC and a slow, steady response (5.6%). These predicted trajectories were modelled without membership predictors. Table A2. Univariable Membership Predictor Testing. * Class 1 was high baseline AJC slow response, class 2 was low baseline AJC early and sustained (plateau) response, class 3 was moderate baseline AJC and steady response. Table A3. Three-class latent classes probability of membership with baseline AJC as membership predictor. Table A4. Distribution of median AJC (25-75th percentile) for participants classified with ≥0.80 probability into the 3 latent classes (n2 = 450). Figure A2. Distribution of median active joint count (AJC) over time by latent classes of AJC, predicted by baseline AJC (n2 = 450). Legend: Class 1 was high baseline AJC slow response, class 2 was low baseline AJC early and sustained plateaued response, class 3 was moderate baseline AJC and progressive response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.809 | 0.040 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".